





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_		:  List[str]		 =						logging.get_logger(__name__)

A_		:  Dict		 =						{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''mobilenet_v1'''

   def __init__(							self	,		__SCREAMING_SNAKE_CASE=3	,		__SCREAMING_SNAKE_CASE=2_2_4	,		__SCREAMING_SNAKE_CASE=1.0	,		__SCREAMING_SNAKE_CASE=8	,		__SCREAMING_SNAKE_CASE="relu6"	,		__SCREAMING_SNAKE_CASE=True	,		__SCREAMING_SNAKE_CASE=0.999	,		__SCREAMING_SNAKE_CASE=0.02	,		__SCREAMING_SNAKE_CASE=0.001	,		**__SCREAMING_SNAKE_CASE	,		):
    super().__init__(**__SCREAMING_SNAKE_CASE       )

    if depth_multiplier <= 0:
     raise ValueError("""depth_multiplier must be greater than zero."""       )

    snake_case__     :					Optional[Any]    =  num_channels
    snake_case__     :					Tuple    =  image_size
    snake_case__     :					str    =  depth_multiplier
    snake_case__     :					Tuple    =  min_depth
    snake_case__     :					List[Any]    =  hidden_act
    snake_case__     :					int    =  tf_padding
    snake_case__     :					Tuple    =  classifier_dropout_prob
    snake_case__     :					Optional[Any]    =  initializer_range
    snake_case__     :					List[Any]    =  layer_norm_eps



class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      version.parse('''1.11'''							)

   @property
   def 		__UpperCamelCase						(							self       ):
    return OrderedDict([("""pixel_values""", {0: """batch"""})]       )

   @property
   def 		__UpperCamelCase						(							self       ):
    if self.task == "image-classification":
     return OrderedDict([("""logits""", {0: """batch"""})]       )
    else:
     return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]       )


   @property
   def 		__UpperCamelCase						(							self       ):
    return 1e-4








class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case       ):
         snake_case											=							n
         snake_case											=							[None] * self.n
         snake_case											=							0  # index of the first element
         snake_case											=							0
         snake_case											=							0
    def __len__(			self       ):
         return self.size
    def 						a_     (			self       ):
         return self.size == 0
    def 						a_     (			self       ):
         return False if self.is_empty() else self.array[self.front]
    def 						a_     (			self     ,   __snake_case       ):
         if self.size >= self.n:
              raise Exception('''QUEUE IS FULL'''       )

         snake_case											=							data
         snake_case											=							(self.rear + 1) % self.n
         self.size += 1
         return self




    def 						a_     (			self       ):
         if self.size == 0:
              raise Exception('''UNDERFLOW'''       )

         snake_case											=							self.array[self.front]
         snake_case											=							None
         snake_case											=							(self.front + 1) % self.n
         self.size -= 1
         return temp




